Application of Time Series Analysis in the Daily Stock Exchange Data

2018 ◽  
Vol 9 (12) ◽  
pp. 1915-1930
Author(s):  
Mohankumari C ◽  
Vishukumar M ◽  
Nagaraja Rao Chillale
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Series Analysis ◽  
Exchange Data

scholarly journals Forecasting variance of NiftyIT index with RNN and DNN

2022 ◽  
Vol 2161 (1) ◽  
pp. 012005
Author(s):  
C R Karthik ◽  
Raghunandan ◽  
B Ashwath Rao ◽  
N V Subba Reddy
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Network Models ◽  
Training Data ◽  
Complex Data ◽  
Neural Network Models ◽  
Series Analysis

Abstract A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.

scholarly journals Penerapan WEMA dalam Peramalan Data IHSG

2013 ◽  
Vol 5 (2) ◽  
pp. 63-66
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Average Method ◽  
Stock Exchange ◽  
Moving Average ◽  
Composite Index ◽  
Market Change ◽  
Series Analysis ◽  
Future Data ◽  
Technical Indicator

One of the most popular technical indicator used in time series analysis for predicting future data is the Moving Average method. During its’ development, many variation and implementation have been made by researchers, one of them is the Weighted Exponential Moving Average (WEMA) which is introduced by Hansun.In this paper, we will try to implement the WEMA method on one of stock market change indicator in Indonesia, i.e. the Jakarta Stock Exchange (JKSE) composite index data. The research is continued by calculating the accuracy and robustness of WEMA method, using MSE and MAPE criteria. The result shows that the WEMA method can be used to predict JKSE data and it’s quite accurate. Kata kunci—time series analysis, JKSE, moving average, WEMA

scholarly journals Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

2015 ◽  
Vol 622 ◽  
pp. 012022
Author(s):  
W S Gayo ◽  
J D Urrutia ◽  
J M F Temple ◽  
J R D Sandoval ◽  
J E A Sanglay
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Analysis

scholarly journals Backpropagation Neural Networks Implementation for JKSE Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 9902-9905
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Stock Exchange ◽  
Promising Result ◽  
Composite Index ◽  
Experimental Result ◽  
Market Change ◽  
Series Analysis ◽  
Backpropagation Neural Networks

Neural networks is a type of soft computing methods that widely has been used and implemented in many fields, including time series analysis. One of the goals of time series analysis is to predict future data value.In this study, we try to implement another approach using the backpropagation neural networks method to forecast the Jakarta Stock Exchange (JKSE) composite index data, which is one of the stock market change indicators in Indonesia.The study then is continued by calculating the accuracy and robustness levels of Backpropagation NN in forecasting JKSE data. The experimental result on the case taken shows an encouraging and promising result.

scholarly journals Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0177652 ◽  
Author(s):  
Arthur Matsuo Yamashita Rios de Sousa ◽  
Hideki Takayasu ◽  
Misako Takayasu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Foreign Exchange ◽  
Series Analysis ◽  
Exchange Data

scholarly journals PENGARUH KARAKTERISTIK PERUSAHAAN TERHADAP STRUKTUR MODAL DINAMIS (PENDEKATAN NILAI PASAR) DI BURSA EFEK INDONESIA

2017 ◽  
Vol 15 (3) ◽  
Author(s):  
Herman Ruslim
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Capital Structure ◽  
Stock Exchange ◽  
Method Of Moment ◽  
Series Analysis ◽  
Growth Opportunity ◽  
Generalized Method Of Moment ◽  
Firm Characteristic ◽  
Dynamic Capital Structure

In financing its activity, a firm depends on its capital structure. Although the nature of financing activities through debt and equity is dynamic, underlying motivation for selecting proper capital structure remain a puzzle. This research attempts to study the effect of stock return and the firm’s characteristics on dynamic capital structure. This research used a sample of the 56 companies, which are listed in Indonesia Stock Exchange (BEI) of the period from 1998 to 2007. The analysis method used in this research was time series analysis with generalized method of moment technique (GMM). The research results indicated that firm did not make adjustment to achieve the target capital structure refering to tradeoff theory, based on time series analysis it is concluded that firm characteristic has significant effect on dynamic capital structure such as profitability, retained earning, long term debt and growth opportunity. It was found that the firm characteristic in size and operating risk does not have a significant effect on the dynamic capital structure. Despite of these, the firm characteristics have a significant effect simultaneously on the dynamic capital structure.

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